title: Impact of patient-level risk adjustment on the findings about nurse staffing and 30-day mortality in veterans affairs acute care hospitals.
abstract: BACKGROUND
  Studies about nurse staffing and patient outcomes often lack adequate risk adjustment because of limited access to patient information.
  
  
  OBJECTIVE
  The aim of this study was to examine the impact of patient-level risk adjustment on the associations of unit-level nurse staffing and 30-day inpatient mortality.
  
  
  METHODS
  This retrospective cross-sectional study included 284,097 patients discharged during 2007-2008 from 446 acute care nursing units at 128 Veterans Affairs medical centers. The association of nurse staffing with 30-day mortality was assessed using hierarchical logistic models under three levels of risk-adjustment conditions: using no patient information (low), using patient demographics and diagnoses (moderate), or using patient demographics and diagnoses plus physiological measures (high).
  
  
  RESULTS
  Discriminability of the models improved as the level of risk adjustment increased. The c-statistics for models of low, moderate, and high risk adjustment were 0.64, 0.74, and 0.88 for non-ICU patients and 0.66, 0.76, and 0.88 for ICU patients. For non-ICU patients, higher RN skill mix was associated with lower 30-day mortality across all three levels of risk adjustment. For ICU patients, higher total nursing hours per patient day was strongly associated with higher mortality with moderate risk adjustment (p = .0002), but this counterintuitive association was not significant with low or high risk adjustment.
  
  
  CONCLUSIONS
  Inadequate risk adjustment may lead to biased estimates about nurse staffing and patient outcomes. Combining physiological measures with commonly used administrative data is a promising risk-adjustment approach to reduce potential biases.
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA. jhe@kumc.edu
authors.forename: Jianghua
authors.initials: J
authors.lastname: He
authors.forename: Peter L
authors.initials: PL
authors.lastname: Almenoff
authors.forename: John
authors.initials: J
authors.lastname: Keighley
authors.forename: Yu-Fang
authors.initials: YF
authors.lastname: Li
mesh_terms: D000368:Aged; D003422:Critical Care; D003430:Cross-Sectional Studies; D005260:Female; D017052:Hospital Mortality; D006787:Hospitals, Veterans; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009741:Nursing Staff, Hospital / Q000706:statistics & numerical data*; D010561:Personnel Staffing and Scheduling / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D020379:Risk Adjustment; D015983:Selection Bias; D014481:United States; D000078329:Workforce